Our work group is actively involved in numerous coordinated and individual research projects to advance new quantum technologies and quantum materials research. Here we give an overview over projects, research goals, and the consortia behind them.
Enabling QUAntum Information by Scalability of Engineered quantum materials
Many applications in quantum information technology require reliable sources of quantum light. The QuantERA project EQUAISE is rooted in the Applied Quantum Science (AQS) topic and aims at developing an industry-level scalable platform for single-photon sources based on strain-engineered quantum emitters made from two-dimensional semiconductors. The project consortium involves partners from Italy, Poland, Spain, and Germany. Project start on a national level is currently pending. More information can be found on the European QuantERA webpage for EQUAISE.
Quantum photonic hardware for new architectures of quantum-machine learning
Quantum reservoir computing (QRC) is a novel computing concept at the intersection of quantum computing and artificial neural networks. In contrast to gate-based quantum computing, the reservoir ansatz loosens the strict requirements of complete control over. Instead, a loose network of quantum systems that are connected by a random coupling topology is trained to perform computational or classification tasks.
Two great advantages come with the inherent quantumness suggested by PhotonicQRC: As a quantum machine learning ansatz, quantum input can be processed natively without the need to represent it in a classical form. Secondly, the exponential size of the Hilbert space allows to treat complex tasks with extremely small physical networks.
PhotonicQRC (link to the website) is run by a German-French consortium cofunded by the DFG and the ANR, connecting our group in Bremen with experimental partners at TU Berlin, femto-ST (CNRS), and ENS (Paris).
Machine Learning with Quantum Systems
Quantum machine learning is gaining a lot of momentum due to its applicability to NISQ technology. In this project, we aim at exploring the potential of QML algorithms that can be run on available quantum computing hardware and compare their prospects and performance to quantum-artificial neural network based machine learning methods.
The project is funded by the DLR Quantum Computing Initiative within the Quantum Fellowship Programme in a collaboration with Prof. Meike List at the DLR Institute for Satellite Geodesy and Inertial Sensing in Bremen.
Cavity-Moiré Physics of Interlayer Excitons
Within the 2dMP priority program of the DFG, we investigate atomically thin semiconductors that are stacked on top of each other with a twist. Interlayer excitons can form that are optically active composite particles distributed between layers of the heterostructure. Their electronic and optical properties can be engineered by the material combination. At the same time, the twist angle gives rise to a superimposed potential landscape, creating analogies to the Hubbard physics of lattice models.
Together with experimental partners at Universität Oldenburg and TU Berlin, we aim at exploring the tunable optical properties of moiré heterostructures in microcavities to study quantum phase transitions, exciton-polariton condensation, and superradiance effects in a highly-tunable semiconductor platform.
Read more about our work here.
Generation and detection of genuine multipartite entanglement in solid-state cavity-QED systems
Quantum entanglement is an essential resource for protocols in quantum information theory. It describes non-classical correlations among the constituents of a quantum system that defy the notion of local reality. While entanglement amongst two parties is well understood, genuine multipartite entanglement (GME) among N parties is much more elusive and harder to characterize, create, and detect. In this project granted by the Zentrale Forschungsförderung of Uni Bremen on genuine multipartite entanglement (ZF GME), our postdoc Frederik Lohof investigates the possibility to create and maintain entanglement in solid-state cavity QED systems and dives into the question of how the presence of entanglement in those systems can enhance the performance of quantum information tasks and quantum machine learning.
The bmbf funded Germany-wide Quantenrepeater.Link (QR.X) network has the goal to realize a quantum repeater prototype as an enabling technology for future quantum networks, like the quantum internet. Our group acts as the interface between quantum information theory and quantum-repeater protocol development on one side, and the experimental semiconductor hardware platform development on the other side. We are developing methods to simulate quantum repeater protocol implementations with conventional entangled states and with multipartite entangled photonic cluster states.
The Bremen-based DFG graduate school QM3 on Quantum Mechanical Material Modeling synergizes expertise in materials modeling and computational material science in the north of Germany. The ambitious program educates about and advances research on quantum materials modeling. The key question our group is involved with is how to tailor material properties on the atomic scale to enable new functionalities on the macroscopic scale using van der Waals materials.